scholarly journals Ecological Remote Sensing Image Monitoring Method for Vegetation Destruction in Waterfront Greenway Based on Genetic Algorithm

2020 ◽  
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Vegetation Index ◽  
Color Image ◽  
Remote Sensing Image ◽  
Image Method ◽  
Monitoring Method ◽  
Geometric Precision ◽  
Image Monitoring ◽  
Vegetation Damage

<p>Because the traditional remote sensing image monitoring method has the problem of poor ability of capturing vegetation destruction, an ecological remote sensing image method for vegetation destruction in waterfront greenway based on genetic algorithm is proposed. The vegetation index data of waterfront greenway were acquired by remote sensing image, and the pseudo color image was synthesized. The on orbit radiometric calibration and geometric precision correction are carried out for the collected data, and the relationship between vegetation index and vegetation damage of waterfront greenway is analyzed, including meteorological factors, geographical factors and vegetation damage of waterfront greenway. Select the independent variables of the model, establish the ecological remote sensing image monitoring model of the vegetation damage of the waterfront greenway, and realize the ecological remote sensing image monitoring of the vegetation damage of the waterfront greenway. The experimental results show that the acquisition ability of the method based on genetic algorithm is better than that of the traditional method, and it is more suitable for the ecological remote sensing monitoring of the vegetation damage of waterfront greenway.</p>

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

Acquisitions of Vegetation Coverage and Cultivated Land Occupation Ratio of Taiyuan Valley Plain for Example Using CBERS-02B CCD Image

2012 ◽  
Vol 518-523 ◽  
pp. 5663-5667
Author(s):  
Shi Wei Li ◽  
Ji Long Zhang ◽  
Jian Sheng Yang
Keyword(s):  
Remote Sensing ◽  
Water Conservation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Image ◽  
Google Earth ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Land Occupation ◽  
Coverage Ratio

Vegetation covering situation is very important for the quality of air quality, soil and water conservation ability and soil forming in an area. By using the remote sensing image of Taiyuan Valley Plain, the application of Normalized Difference Vegetation Index (NDVI) and unsupervised classification, the vegetation coverage map which includes non-cultivated land disposition and cultivated land disposition was obtained using ERDAS Imagine software. To evaluate the accuracy of the results, 200 points were sampled randomly, the high spatial resolution remote sensing image from Google Earth was used as the reference. The overall classification accuracy is 82%, with the Kappa statistic of 0.81. By counting the totally pixel acreage, it was gotten that the vegetation coverage was 46% and the cultivated land coverage ratio was 31% in the study area.

scholarly journals RESEARCH ON HIGH RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION BASED ON SEGNET SEMANTIC MODEL IMPROVED BY GENETIC ALGORITHM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 415-422
Author(s):  
M. Xue ◽  
B. Wei ◽  
L. Yang
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
High Resolution ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Image Feature ◽  
Support Vector ◽  
Local Optimum ◽  
Semantic Model ◽  
Back Propagation Algorithm

Abstract. SegNet model is an improved model of Full Convolutional Networks (FCN). Its encoder, i.e. image feature extraction, is still a convolutional neural network (CNN). Aiming at the problem that most traditional CNN training uses error back propagation algorithm (BP algorithm), which has slow convergence speed and is easy to fall into local optimum solution, this paper takes SegNet as the research object, and proposes a method of extracting partial weights by using genetic algorithm (GA) to select features of SegNet model, and to alleviate the problem that SegNet is easy to fall into local optimal solution. In the training process of SegNet model, the weight of convolution layer of SegNet model used to extract features is optimized through selection, crossover and mutation of genetic algorithm, and then the improved SegNet semantic model (GA-SegNet model) is obtained by GA. In order to verify the image classification effect of the proposed GA-SegNet model, the same high-resolution remote sensing image data are used for experiments, and the model is compared with maximum likelihood (ML), support vector machine (SVM), traditional CNN and SegNet semantic model without GA improvement. The experimental results show that the proposed GA-SegNet model has the best classification accuracy and effect, which GA overcomes the problem of premature convergence of BP random gradient descent to a certain extent, and improves the classification performance of SegNet semantic model.

Monitoring Method for Laser Via Hole Processing of Printed Circuit Boards Based on Two-Color Method With a High-Speed Video Camera

2020 ◽  
Author(s):  
Wataru Nakagawa ◽  
Ryuta Yamaguchi ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama
Keyword(s):  
High Speed ◽  
Printed Circuit Boards ◽  
Color Image ◽  
Irradiation Time ◽  
Single Pulse ◽  
Video Camera ◽  
Image Method ◽  
Drilling Process ◽  
Monitoring Method ◽  
Direct Laser

Abstract A build-up process is used to manufacture printed wiring boards (PWBs) for high-density circuits. Presently, CO2 laser beams are used to drill blind via holes (BVHs) that connect copper foils. The Cu-direct drilling process has received considerable attention but is problematic because it produces a copper overhang due to the complex processing phenomena. This report focuses on monitoring scattered matter by Cu-direct laser drilling with a high-speed camera and clarifying the factors related to processing quality while verifying the results by CFD (Computational Fluid Dinamics) analysis. Previous research has shown that processing progress can be made from temperature information using the two-color image method that can measure temperature without contact. However, the two-color image method generates noise in the temperature range (500–3000 °C) which is treated in this research. Filtering was possible by using the RGB data of each pixel on the image. By focusing on laser fluence, it became possible to estimate the laser irradiation time that can guarantee the quality in the drilled hole (BVH) in single pulse continuous irradiation.

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